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BackgroundDepression, one of the most frequent non-motor symptoms in Parkinson’s disease (PD), was proposed to be related to neural network dysfunction in advanced PD patients. However, the underlying mechanisms in the early stage remain unclear. The study was aimed to explore the alterations of large-scale neural networks in de novo PD patients with depression.MethodsWe performed independent component analysis (ICA) on the data of resting-state functional magnetic resonance imaging from 21 de novo PD patients with depression (dPD), 34 de novo PD patients without depression (ndPD), and 43 healthy controls (HCs) to extract functional networks. Intranetwork and internetwork connectivity was calculated for comparison between groups, correlation analysis, and predicting the occurrence of depression in PD.ResultsWe observed an ordered decrease of connectivity among groups within the ventral attention network (VAN) (dPD < ndPD < HCs), mainly located in the left middle temporal cortex. Besides, dPD patients exhibited hypoconnectivity between the auditory network (AUD) and default mode network (DMN) or VAN compared to ndPD patients or healthy controls. Correlation analysis revealed that depression severity was negatively correlated with connectivity value within VAN and positively correlated with the connectivity value of AUD-VAN in dPD patients, respectively. Further analysis showed that the area under the curve (AUC) for dPD prediction was 0.863 when combining the intranetwork connectivity in VAN and internetwork connectivity in AUD-DMN and AUD-VAN.ConclusionOur results demonstrated that early dPD may be associated with abnormality of attention bias and especially auditory attention processing. Altered neural network connectivity is expected to be a potential neuroimaging biomarker to predict depression in PD.

To fully exploit the parallelism and efficiency of CMP architecture, improve the efficiency of thread scheduling under CMP architecture and avoid the drop of multi-core processor throughput due to resource contention, a thread scheduling strategy - IFWAC based on improved fireworks algorithm is proposed. IFWAC excludes the fireworks with the optimal value of each generation to ensure the effectiveness of the sparks and avoid the waste of computing resources; The nonlinear inertia weighting factor is applied to the dimension of the rejection operation. So that the algorithm in the early iteration has a stronger global exploration capabilities. In the latter part of the iteration can be meticulous optimization. The IFWAC, the fireworks algorithm and the genetic algorithm are used to perform the scheduling experiment on the Matlab platform. The experimental results show that the IFWAC has obvious advantages in the convergence speed and the accuracy of the solution. The IFWAC improve the throughput of multi-core processors and has a wide range of research value.